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Description
Shock Bonnet Stay RH for Mercedes-Benz C-Class & C-Class T-Model - 390057Vehicle Fitment & Part Details The Shock Bonnet Stay RH is listed for the Mercedes Benz C Class (2007 2014). Size: 648mm. Confirm compatibility by matching the listed fitment details. Verify VIN engine. Key Details SKU 390057 Component Shock Bonnet Stay Specs Size: 648mm Primary Fitment Mercedes Benz C Class (2007 2014) Listing Highlights Listed for selected Mercedes Benz applications shown in the fitment table. Size: 648mm. Vehicle Fitment Includes
Vehicle Fitment & Part Details
The Shock Bonnet Stay RH is listed for the Mercedes-Benz C-Class (2007-2014). Size: 648mm. Confirm compatibility by matching the listed fitment details. Verify VIN/engine.
Key Details
- SKU
- 390057
- Component
- Shock Bonnet Stay
- Specs
- Size: 648mm
- Primary Fitment
- Mercedes-Benz C-Class (2007-2014)
Listing Highlights
- Listed for selected Mercedes-Benz applications shown in the fitment table.
- Size: 648mm.
Vehicle Fitment
Includes 34 supplied applications covering Mercedes-Benz. Use the full table below to confirm model, chassis, year range, engine and power before ordering.
View Full Vehicle Fitment (34 applications)
| Make | Model | Chassis | Years | Engine | Power |
|---|---|---|---|---|---|
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 180 CGI 204.049 M 271.820 | 115 kW / 156 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 180 Kompressor 204.044, 204.045 M 271.910 | 115 kW / 156 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 180 Kompressor 204.046 M 271.952 | 115 kW / 156 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 200 CDI 204.001 OM 651.913 | 100 kW / 136 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 200 CGI 204.048 M 271.820; M 271.860 | 135 kW / 184 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 200 Kompressor 204.041 M 271.950 | 135 kW / 184 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 220 CDI 204.002 OM 651.911 | 125 kW / 170 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 220 CDI 204.008 OM 646.811 | 125 kW / 170 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 250 CDI 204.003 OM 651.911 | 150 kW / 204 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 250 CGI 204.047 M 271.860 | 150 kW / 204 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 280 204.054 M 272.947 | 170 kW / 231 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 300 204.054 M 272.947 | 170 kW / 231 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 300 204.055 M 276.957 | 185 kW / 252 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 320 CDI 204.022 OM 642.960 | 165 kW / 224 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 350 204.056 M 272.961 | 200 kW / 272 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 350 4-matic 204.087 M 272.971 | 200 kW / 272 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 350 CDI 204.022 OM 642.960 | 165 kW / 224 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 350 CDI 204.023 OM 642.834 | 195 kW / 265 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 350 CDI 204.025 OM 642.830 | 170 kW / 231 HP |
| Mercedes-Benz | C-CLASS | W204 | 2007-2014 | C 350 CDI 4-matic 204.089 OM 642.961 | 165 kW / 224 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 180 CGI 204.249 M 271.820 | 115 kW / 156 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 180 Kompressor 204.245 M 271.910 | 115 kW / 156 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 180 Kompressor 204.246 M 271.952 | 115 kW / 156 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 200 CDI 204.201 OM 651.913 | 100 kW / 136 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 200 CGI 204.248 M 271.820; M 271.860 | 135 kW / 184 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 200 Kompressor 204.241 M 271.950 | 135 kW / 184 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 220 CDI 204.202 OM 651.911 | 125 kW / 170 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 220 CDI 204.208 OM 646.811 | 125 kW / 170 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 230 204.252 M 272.921 | 150 kW / 204 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 250 CDI 204.203 OM 651.911 | 150 kW / 204 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 250 CGI 204.247 M 271.860 | 150 kW / 204 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 280 204.254 M 272.947 | 170 kW / 231 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 320 CDI 204.222 OM 642.960 | 165 kW / 224 HP |
| Mercedes-Benz | C-CLASS T-Model | S204 | 2007-2014 | C 350 204.256 M 272.961 | 200 kW / 272 HP |
Fitment Notes
- Always match vehicle details before ordering. Verify VIN/engine.
Compatibility Verification Notes
- Confirm compatibility using VIN, engine code, chassis / platform, OE reference and original part comparison before ordering.
- Match the supplied fitment details to your vehicle, including model, year range and any listed engine or chassis information.
- Fitment data may vary by production date, market, import history and engine variant.
- This listing is for part identification and compatibility checking only. Installation must be carried out according to the vehicle manufacturer's service information by a suitably qualified person.
Common Questions
Will the Shock Bonnet Stay RH fit my Mercedes-Benz C-Class?
This part (390057) is listed for the fitments shown on this page. Confirm by matching the fitment details. Verify VIN/engine.
How do I confirm fitment if there are multiple variants?
Compare the supplied fitment details, original part details and any listed variant information before ordering. Verify VIN/engine.
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
- Except Preorder products are shipped in 48 hours.
- Delivery to the USA:
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- If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
- Final sale items are not eligible for returns or exchanges.
- To process your return/exchange, please contact us at [email protected]
- Please click here for more details>>> Return & Exchange Policy
4.4 ★★★★★
Based on 1726 reviews
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Product Reviews
★★★★★ 4
Title: Really Good Book for Learning LLMs
Format: Paperback, Format: Paperback
I picked up this book after struggling with LLM implementation at work. Ken Huang explains things clearly without too much technical jargon.
The book covers everything from data preparation to building AI agents. I especially liked the chapters on RAG and prompting techniques - they helped me improve my current projects. The code examples actually work, which is nice.
Some parts are pretty advanced, so you need basic Python knowledge. I had to read a few chapters twice to fully get it. The fairness and bias detection section was eye-opening.
Good practical advice throughout. Not just theory - real solutions you can use. Worth the money if you're serious about LLM development.
Recommended for anyone building AI systems professionally.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on July 25, 2025
★★★★★ 5
a comprehensive guide at the intersection of generative AI and cybersecurity
Format: Kindle
This book blends deep theoretical foundations with practical frameworks and forward-looking strategies. From adversarial risk models to actionable guidance using OWASP Top 10 for LLMs and the NIST AI RMF, it offers both technical depth and operational clarity.
What makes it stand out is its balance of academic rigor and real-world CISO insights, providing a holistic perspective on securing GenAI systems. While it leans enterprise-focused, the content remains accessible to security engineers, risk managers, and policy leaders alike.
Generative AI Security is a timely and essential read for anyone working to deploy GenAI responsibly—building systems with both power and integrity in today’s fast-evolving threat landscape.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on July 2, 2025
★★★★★ 1
Light on substance and heavy on flaws
Format: Paperback
The book has a great list of topics, but fails to provide much substance any of them.
Most of the provided code is just comments that avoid the actual crux of the issues being discussed. (e.g. #implement the logic to validate XYZ - while the whole point of this chapter is teach how the heck we validate XYZ!)
Some parts are plain wrong, for example the part on Graph based RAG is fundamentally flawed as it assumes the text embedding and the graph embedding are in the same latent space. (This is one of many more examples).
Seems like the book was rushed, and the author has limited hands on experience (if any).
At least we know based on the amount of flaws that it was not written by an LLM
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 31, 2025
★★★★★ 5
Excellent book to truly understand LLM design patterns
Format: Paperback
I just finished reviewing Ken Huang's pocket book on LLM Design Patterns, and WOW what an amazing resource! This book is excellent if you want to truly understand how to create and enhance intelligent AI language models, all that in your pocket!
Ken makes the difficult things seem surprisingly easy, and that's the real MAGIC.
- How to prepare your data for training by making it extremely clean.
Developing the brains: the practical aspects of training, optimizing, and maintaining your models.
- Learn amazing prompting techniques (such as Chain-of-Thought and Tree-of-Thoughts) to improve your AI's reasoning and problem-solving abilities.
Learn everything there is to know about RAGs so that your LLM can incorporate outside expertise.
- It also delves into creating "agentic" AI that is capable of action and planning (not only simple plan and execute but also enhanced techniques like ReWoo!)
Really, this feels like a useful toolkit, so Ken thank you for that resource
Thanks, Idan Habler
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on June 9, 2025
★★★★★ 3
A Broad Overview, But Light on Modern Fine-Tuning
Format: Paperback
I'm currently really interested in fine-tuning LLMs and recently completed my first LoRA-based fine-tuning on a quantized model. I came to this book looking for more detail on fine-tuning. While it touches on the topic, I found the content didn’t quite align with the current state of the field in 2025. Techniques like LoRA, QLoRA, and PEFT weren’t really covered, and the material leaned more toward what I think are older or lower level approaches. That made it harder to connect with what I’m actually working on.
That said, when I shifted to other chapters — like the sections on prompt engineering techniques such as Chain of Thought (CoT) and Tree of Thought (ToT) — I found more value. These sections were clearer, and I picked up a few practical insights, like using few-shot examples that walk through the CoT reasoning process. That’s not something I’ve tried before, and I can see how it might help smaller models that struggle with any type of reasoning tasks.
Overall, the book feels more like a broad overview of all LLM concepts. For someone exploring many topics across the LLM ecosystem, it offers a wide-ranging introduction. But for readers like me who are actively trying to learn and apply techniques like fine-tuning and quantization, it may leave you wanting up-to-date guidance.
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Reviewed in the United States on August 10, 2025